Construction of the Site Index Model of Secondary Populus Davidiana × Betula Platyphylla Mingled Forest in Northern Hebei Mountains
In broad-leaved mingled forest with multiple tree species and complex structures,constructing a high-precision site index model based on the diameter at breast height of dominant trees is a key scientific challenge in evaluating the site quality of these forests.The Populus davidiana and Betula platyphylla broad-leaved mingled forest in Mulan Weichang,Hebei Province is selected as the research object.Based on the survey data from 70 standard plots(each standard land area is 0.06 hm2),using the nonlinear least squares method,BP neural network,and nonlinear mixed-effects model,three parameter estimation methods are used to construct the site index models of P.davidiana and B.platyphylla broad-leaved mingled forest.Using Mean Square Error(MSE),Root Mean Square Error(RMSE),Mean Percentage Bias(MPB),Coefficient of Determination(R2),Adjusted Coefficient of Determination(R2dj),Akaike Information Criterion(AIC),Bayesian Information Criterion(BIC),and Negative Twice the Log-Likelihood(-2LL),the impact of different parameter estimation methods on model prediction accuracy is compared.The results show that:1)Among the five candidate equations,the logistic equation with the diameter at breast height of dominant trees as the independent variable is the optimal site index base model for the mingled P.davidiana and B.platyphylla broad-leaved forest,and the MSE,RMSE,MPB,R2,R2adj of its model are 9.071 7,2.269 6,11.972 9,0.583 8,0.569 9,respectively.2)Compared with the nonlinear least squares method and BP neural network,the site index model of P.davidiana and B.platyphylla broad-leaved mingled forest based on the nonlinear mixed-effects has higher prediction accuracy.The MSE,RMSE,MPB,R2,R2adj of the model are,5.477 4,1.779 4,9.161 4,0.782 8,0.761 7,respectively.The site index model constructed with the diameter at breast height of dominant trees as the independent variable can be used to evaluate the site quality of mingled forests.
site indexBP neural networknonlinear mixed-effects modelbroad-leaved mingled forest
NETL
NSTL
万方数据
